Lead the AI quality roadmap, progressively integrating AI across all QA functions.
Evaluate and implement AI tools for test generation, self-healing suites, and intelligent defect triage.
Define AI-specific KPIs to track the shift from manual to AI-driven quality practices.
Lead the transition of QA engineers from manual testers to quality architects who orchestrate AI-driven systems.
Upskill the QA org through targeted training in prompt engineering, AI orchestration, and meta-QA practices.
Drive innovation by fostering an experimental culture focused on piloting AI capabilities, measuring results, and rapid iteration.
Own and deliver measurable improvements in customer-reported defects and critical severity rates.
Implement AI prioritization to focus testing on high-impact code changes and defect-prone areas.
Transition from current manual burdens to near-zero through AI-generated automation and intelligent execution.
Embed quality practices early in the SDLC, shifting the focus from "test-and-find" to "prevent-and-detect."

10+ years in QA with 5+ years leading teams of 20+ engineers.
Proven track record of fundamentally changing quality delivery (e.g., manual to automated/AI-augmented) rather than just maintaining teams.
Hands-on experience with AI/ML testing tools; ability to critically evaluate and implement emerging AI technology.
Strong knowledge of automation frameworks, CI/CD pipelines, and DevOps/Agile methodologies.
Experience testing complex, security-sensitive products across cloud, on-prem, and hybrid environments.
Ability to define and drive KPIs that create accountability and measurable outcomes.
Strong leadership skills to navigate teams through significant organizational shifts and build cross-functional buy-in.
BS in Computer Science or related field (Master’s preferred).

Director, QA

Key skills